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1. The Unseen Impact of Inclusive Professional Development and Pedagogic Training on Underestimated Minority Graduate Students

   https://doi.org/10.58021/S5NP4V  

   Lui, Lauren M ; Roth, Danica L ; Roybal-Jungemann, Gabriel ; Irvine, Lelemia ( January 2022 , Institute for Scientist & Engineer Educators (ISEE))

   Seagroves, Scott ; Barnes, Austin ; Metevier, Anne ; Porter, Jason ; Hunter, Lisa (Ed.)

   Ostensibly, the main goal of the ISEE Professional Development Program (PDP) is to teach scientists and engineers how to be intentional, inclusive educators by experiencing and designing inquiry-based learning activities. However, the PDP program has many indirect, positive effects on its participants as well, including building community and a sense of STEM identity, fluency to understand and discuss diversity, equity, and inclusion topics, and recognizing the importance of psychological safety in learning, academia, and industry. We present four narratives from past participants with underestimated minority identities, who discuss how the PDP program had a positive impact on their growth as scientists and engineers. In each case, the PDP provided critical tools, knowledge or support that enabled their success as graduate students and into their respective career and life journeys. 

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2. An Empirical Approach to Understanding Data Science and Engineering Education

   https://doi.org/10.1145/3344429.3372503  

   Raj, Rajendra K. ; Parrish, Allen ; Impagliazzo, John ; Romanowski, Carol J. ; Aly, Sherif G. ; Bennett, Casey C. ; Davis, Karen C. ; McGettrick, Andrew ; Pereira, Teresa Susana ; Sundin, Lovisa ( July 2019 , Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education)

   As data science is an evolving field, existing definitions reflect this uncertainty with overloaded terms and inconsistency. As a result of the field’s fluidity, there is often a mismatch between what data-related programs teach, what employers expect, and the actual tasks data scientists are performing. In addition, the tools available to data scientists are not necessarily the tools being taught; textbooks do not seem to meet curricular needs; and empirical evidence does not seem to support existing program design. Currently, the field appears to be bifurcating into data science (DS) and data engineering (DE), with specific but overlapping roles in the combined data science and engineering (DSE) lifecycle. However, curriculum design has not yet caught up to this evolution. This working group report shows an empirical and data-driven view of the data-related education landscape, and includes several recommendations for both academia and industry that are based on this analysis. 

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3. Characterizing the landscape of plant science careers in the United States II: Academic perspectives

   https://doi.org/10.1002/ppp3.10374  

   Walsh, Lisa L. ; Parsley, Kathryn M. ; Sidoti, Brian J. ; Callis‐Duehl, Kristine ; Hove, Alisa A. ; Liu, Hong ; Bruce‐Opris, Hannah ; Gonzalez, Roxana ; Ospina, David ; Uzcategui, Mia ; et al ( May 2023 , PLANTS, PEOPLE, PLANET)

   Botanical careers are more important than ever, given that environmental challenges such as climate change and deforestation threaten plants daily and because plants contribute to solutions to these problems. Plants act as our sources of food, medicine, textiles, and oxygen, which means finding ways to mitigate these environmental challenges is crucial. Despite this, little is known about what career opportunities exist for botanists outside of academia and how well academia is training graduate students for these careers. This study centers on the current state of academic botanical careers and how well students completing post‐baccalaureate degrees (herein referred to as graduate students) are being prepared to fill careers within the botanical workforce.

   Plant science plays a crucial role in our society and in ongoing efforts to address many global challenges, including food insecurity and climate change. Despite a predicted increase in botanical career opportunities, little is known about how well academia is training graduate students for careers outside of academia.

   To further our understanding of the current state of academic training for botanical careers, we surveyed 85 faculty and 40 graduate students working in academia in the plant sciences in the United States.

   We found that the top challenges to university professors in academia are lack of support staff and funding, whereas students completing their post‐baccalaureate degrees cited finances and lack of supportive mentoring as their top challenges. Despite the fact that most graduate students surveyed wanted a career at a research‐intensive university, many botanists in academia are retiring without being replaced by more botanists. Faculty expertise is also misaligned with needs from industry and government employers, causing challenges to training graduate students for these careers outside of academia. Although our data point to a lack of career opportunities within academia, we also note that current graduate student education still emphasizes such careers and is not properly preparing graduate students for the careers they are more likely to obtain within the private and government sectors.

   We discuss the implications of these findings and present several recommendations for preparing future generations of plant scientists for more realistic career trajectories.

    

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4. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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5. Accelerating the Broad Implementation of Verification and Validation in Computational Models of the Mechanics of Materials and Structures

   The Minerals Metals & Materials Society, Inc. ( October 2020 , Accelerating the Broad Implementation of Verification and Validation in Computational Models of the Mechanics of Materials and Structures)

   This report is intended to provide value to scientists, engineers, software developers, designers, analysts, regulators, students, and other stakeholders associated with (or intending to work with) computational models related to the mechanics of materials and structures (MOMS). This includes both modelers and experimentalists within the materials science and engineering, mechanical engineering, solid mechanics, structural dynamics, and related communities, spanning academic, industrial, and government affiliation sectors. This report was written with two types of people in mind: novices who have little or no prior experience in robust verification and validation (V&V) and associated/inseparable uncertainty quantification (UQ) practices, and those who have some V&V/UQ experience, but want to establish more rigorous practices. More specifically, researchers, developers, and students associated with materials (both structural and soft materials) and solid mechanics modeling, who utilize advanced computation, materials data, and/or experimental validation tools, should find the information in this report especially useful. It is critical that the community widely adopts robust V&V/UQ practices in order to improve trust, reduce risk, and improve the reliability of MOMS computational models. Beyond practitioners in this field, other stakeholders who can influence the future of advanced computational modeling associated with MOMS should find this report useful, as well. This includes individuals who support financial and/ or time investments in science and technologies surrounding computational modeling, such as funding officers and other decision-makers at federal agencies, and leaders/managers in industry. Educators teaching undergraduate and graduate courses related to MOMS, as well as department heads and/or deans within the relevant disciplines, also could use the information in this report to advance associated curricula and enhance research products. 

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